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Bioinformatics Advance Access published online on April 19, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn120
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A Note on the False Discovery Rate and Inconsistent Comparisons between Experiments.

Roger Higdon 1, Gerald van Belle 2 and Eugene Kolker 1,3,*

1 Seattle Children's Hospital and Regional Medical Center, Seattle, WA 98101, USA.; 2Departments of Biostatistics and Environmental and Occupational Health Sciences, University of Washington, Seattle, WA; and 3Division of Biomedical Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, USA Tel: 206.884.7170; Fax: 206.884.7175; E.K.'s Email: Eugene.Kolker{at}seattlechildrens.org. R.H.'s Email: Roger.Higdon{at}seattlechildrens.org

*To whom correspondence should be addressed. Eugene Kolker, E-mail: Eugene.Kolker{at}seattlechildrens.org


  Abstract

Motivation: The false discovery rate (FDR) has been widely adopted to address the multiple comparisons issue in high throughput experiments such as microarray gene expression studies. However, while the FDR is quite useful as an approach to limit false discoveries within a single experiment, like other multiple comparison corrections it may be an inappropriate way to compare results across experiments.

This paper uses several examples based on gene expression data to demonstrate the potential misinterpretations that can arise from using FDR to compare across experiments. Researchers should be aware of these pitfalls and wary of using FDR to compare experimental results. FDR should be augmented with other measures such as p-values and expression ratios. It is worth including standard error and variance information for meta-analyses and, if possible, the raw data for re-analyses. This is especially important for high throughput studies because data are often reused for different objectives, including comparing common elements across many experiments. No single error rate or data summary may be appropriate for all of the different objectives

Associate Editor: Prof. Martin Bishop

Received on January 18, 2008; revised on March 14, 2008; accepted on April 1, 2008

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